I’m interested in the relationship between air pollution and birth outcomes. I’m looking at California specifically, and we are proposing to use birth records for outcome classification, and living (and being pregnant) within 5km of a power plant that was retired within 2000-2015 as the exposure variable. Our hypothesis is that women living and pregnant within 20km when the power plants were operating will more likely have pre-term births or babies that are small for gestational age compared to women living within 20km of power plants after power plants were retired. From this we could do a multi-stage sampling scheme. First, we would identify our clusters consisting of women who gave birth with residential addresses within 20km of a power plant that was operating between 2000 and 2015. Then we would stratify our sample to women who were pregnant before versus after the power plant was retired. Random sampling reduces the likelihood of selection bias and minimizes the potential, helping identify causal effects than using a non-probabilistic sampling. This method is advantageous over simple random sampling because it ensures that women living near the retired power plants are included in the study. However, because we are limiting our study to women living within 20km of a power plant, it could reduce the representativeness of our study population to our target population (California).